The present invention relates generally to a geolocation data discovery system, and more particularly, but not by way of limitation, to a system using data streams of trusted sources already in existence to collect geolocation data and time to plot events.
Spatiotemporal data and correlation of that data with events are important components of fleet management systems, e.g. traffic and/or road condition avoidance, public safety alerts. Such event and alert data is conventionally sent using social media streams such as Twitter® and Nixle®. These alerts and messages often times contain location data, but in textual form for human consumption, e.g. “@NHDOT TRAFFIC ALERT: Emergency road construction between exits 7W and 6 on Southbound Everett Turnpike”. The alert message contains location data associated with the server that is distributing the alert, but not location data associated with the geolocation of the event. Thus, the conventional fleet management systems cannot be synced with navigational services (e.g., the location of the events cannot be mapped onto the navigational maps) because the locational data of the alert is at a centralized server location and not at the location of the alert.
Conventional navigational systems attempt to leverage real-time data to predict the most efficient route (e.g., estimate a state of traffic, delays, accidents, etc.). However, the conventional approaches rely on user inputs (such as a feedback or data drawn from a device) such that the system could be manipulated to output false statements because the data is being processed from untrusted sources. For example, if user inputs or a computer algorithm generating user inputs are enough to flood a system with an accident report in a particular location, the conventional navigation systems will display to users that there is traffic in the area even if there is actually no traffic. Alternatively, the system can be flooded with reports of no traffic in an area with heavy traffic to manipulate navigational systems to guide the users on this path that can potentially contain a high traffic state. This can create security concerns by enabling users to create traffic in predetermined locations.
That is, there is a technical problem in the conventional techniques in that the conventional fleet management techniques manage the system based on either event data from unsecure user inputs and feedback such that the event data can be manipulated to create a false positive of traffic, or allow users to intelligently change a route based on received messages from a central location from a secure input such as government alerts such that it requires intelligent interaction with the navigational system by a user outside of the system capabilities. In other words, there is a technical problem in the conventional techniques that unsecure sources can manipulate data of a fleet management system for a desired outcome that can potentially create risks to society (e.g., creating a traffic situation to prevent emergency services from arriving at a location, etc.).